Uberi/speech_recognition

Features

• Speech-to-Text Integrations - Provides a unified interface to transcribe spoken audio into text via multiple online and offline recognition engines.
• Speech-to-Text Libraries - Provides a unified Python interface for converting spoken audio from microphones or files into written text.
• Long Audio Chunk Transcribers - Processes recorded audio files by segmenting them into manageable chunks for stable transcription.
• Unified Provider Interfaces - Implements a unified provider interface that standardizes communication across diverse cloud-based and local speech recognition engines.
• On-Device Speech Recognizers - Provides a unified interface to local recognition engines for offline speech-to-text transcription.
• Speech Recognition Libraries - Functions as a comprehensive Python library providing a unified API for multiple speech-to-text engines.
• Speech Recognition APIs - Integrates with cloud-based speech recognition APIs to transcribe audio data via external web services.
• Unified Recognition Interfaces - Bridges Python applications to various cloud APIs and local engines through a consistent interface.
• Offline Media Transcribers - Converts audio to text using local recognition engines to enable transcription without an internet connection.
• Real-Time Microphone Captures - Captures live audio from a device microphone to detect and transcribe spoken phrases on the fly.
• Audio Captures - Captures live audio from the device microphone for real-time or background speech-to-text conversion.
• Camera and Microphone Recorders - Captures live audio input from physical microphones with configurable sample rates and device indices.
• Transcription Engine Adapters - Features modular adapters that allow interchangeable use of diverse online and offline speech recognition engines.
• Speech-to-Text API Wrappers - Acts as a wrapper for various cloud-based speech-to-text APIs to provide a consistent transcription interface.
• Custom Phrase Detection - Automatically identifies the start and end points of spoken phrases within an audio source.
• Multilingual Transcription - Supports transcription of spoken words across various languages and regional dialects.
• Audio Trigger Detection - Monitors audio streams in the background for spoken phrases to trigger specific system callbacks.
• Multilingual Support - Provides support for transcribing audio in multiple languages through various language packs.
• Ambient Noise Calibration - Analyzes background noise levels to dynamically optimize the sensitivity of voice activity detection thresholds.
• Audio Processing Frameworks - Implements a framework for capturing microphone input and managing audio file formats for transcription.
• Background Thread Dispatchers - Utilizes background thread dispatching to listen for speech and trigger callbacks without blocking the main execution.
• Integration Adapters - Provides architectural abstraction layers to decouple various speech recognition engine integrations from the core application logic.

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